Image processing device, image processing method, image processing program, and integrated circuit

ABSTRACT

The grayscale of an input signal is converted without amplifying noise components thereof. A grayscale conversion portion performs grayscale conversion on an input signal IS to create a converted signal TS, a noise reduction degree determining portion determines a noise reduction degree NR that expresses a strength of noise reduction processing to be applied to the converted signal based on the input signal IS and the converted signal TS, and a noise reducing portion executes noise reduction processing on the converted signal TS based on the noise reduction degree NR. By doing this, it is possible to convert the grayscale of the input signal without enhancing the noise.

This application is a continuation of application Ser. No. 11/806,811,which was filed Jun. 4, 2007.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to image processing devices, imageprocessing methods, image processing programs, and integrated circuits,and in particular relates to image processing devices, image processingmethods, image processing programs, and integrated circuits that performgrayscale conversion without enhancing noise in the input signal.

2. Description of the Related Art

In general, image-capturing devices and display devices performgrayscale conversion for the purpose of correcting the brightness or thecontrast of an input signal. Pixel unit processing and processing inwhich the surrounding region is referenced are known as examples ofgrayscale conversion.

Pixel unit processing is conversion processing that is performed basedon only the pixel value of a target pixel, without referencing pixelsother than the target pixel. A specific example is gamma correction,which is adopted for captured images in order to cancel out thephotoelectric conversion characteristics of CRT display devices.

On the other hand, processing in which the surrounding region isreferenced is conversion processing that is performed in reference tonot only the pixel value of a target pixel but also to the pixel valuesof the region surrounding the target pixel or the pixel values of theentire image. A specific example is histogram equalization, in which thedistribution of the frequency with which pixel values appear in an inputsignal is found, and grayscale conversion is performed on the inputsignal by assigning a wide range of grayscale to frequently appearinggrayscale levels in the input image (with histogram equalization, ifthere is a narrow range of grayscale values that appear frequently inthe input signal (for example, a case in which there are 5 to 20grayscale values in the case of 8-bit data grayscale values), thengrayscale conversion is performed to obtain grayscale values over a widerange (for example, 10 to 120 grayscale values) in the output signal).Another specific example is visual processing, in which conversionprocessing is performed based on the pixel value of the target pixel andthe mean value of the pixel values of the surrounding region (mean pixelvalue).

By applying these grayscale conversions to an input signal, it ispossible to obtain a converted signal in which the perception (feeling)of the brightness or the contrast is improved. However, as in the caseof gamma correction, when a pixel with a small pixel value is processedwith a high gain, the very tiny noise component in the input signal isamplified and the S/N ratio is significantly getting worse. Onetechnology for remedying this issue that has been disclosed is thetechnology of performing noise reduction processing on the convertedsignals for pixels with a small pixel value (for example, JP2001-309177A). With this technology, noise reduction is performed onlyon pixels in which the slope of the gamma curve, which expresses theinput/output characteristics of the grayscale conversion, is greaterthan a predetermined threshold. As in gamma correction, in cases wherethe input/output characteristics of the grayscale conversion aredetermined in advance, those input/output characteristics havemonotonically increasing properties, and the slope of the input/outputcharacteristics curve expressing those input/output characteristics hasthe property of monotonically decreasing, the gain increases the lowerthe level of the input signal (the level of the input signal with asmall grayscale value). Thus pixels in which the S/N ratio becomes poor,that is, pixels with a small pixel value that are processed with a highgain, are specified by detecting input signals that are below thethreshold value. By performing noise reduction on the pixels that havebeen specified in this way, it is possible to improve the deteriorationof the S/N ratio in dark areas.

However, with processing in which the surrounding region is referenced,such as histogram equalization and visual processing, the input/outputcharacteristics of the grayscale conversion are changed for each image,or for each pixel, according to the frequency distribution of the pixelvalues or the mean pixel values around the target pixel of the inputsignal. When a conventional approach such as gamma correction is adoptedin a processing method in which the surrounding region is referenced, itis not possible to pre-calculate the pixel values in which the slope ofthe input/output characteristics of the grayscale conversion is smallerthan a predetermined threshold value, and thus if this is adopted formoving pictures, it is necessary to search the input/outputcharacteristics of the grayscale conversion that are obtained each frame(or each field) or each pixel, and calculate a pixel level (pixel value)of the input signal that corresponds to the threshold value of the slopeof the input/output characteristics curve for grayscale conversion thathas been obtained. This processing, however, requires a large amount ofcomputations.

In general grayscale conversion, there is no guarantee that the slope ofthe input/output characteristics curve for the input/outputcharacteristics will decrease monotonically like with gamma correction,and thus with regard to the standard for determining a pixel value inwhich the slope is equal to or less than the threshold value, there is apossibility that it may not be possible to uniquely determine the rangeof the pixel levels (pixel values) of the input signal for which toadopt noise reduction.

Threshold processing simply is processing for switching whether or notto adopt noise reduction for a given pixel, and thus it is not possibleto carry out noise reduction at a strength that is suited for eachpixel.

It is an object of the invention to provide an image processing device,an image processing method, an image processing program, and anintegrated circuit with which it is possible to execute noise reductionat a different strength for each target pixel, in accordance with thedegree of the deterioration of the S/N ratio due to the amplification ofnoise components, not only in the case of grayscale conversion whereonly the target pixel is referenced, but also in the case of grayscaleconversion in which the region around the target pixel is referencedalso.

SUMMARY OF THE INVENTION

A first aspect of the invention is an image processing device thatincludes a grayscale conversion portion that performs grayscaleconversion on an input signal that is made from pixel data that form animage to obtain a converted signal, a noise reduction degree determiningportion that determines a noise reduction degree for the convertedsignal based on the input signal and the converted signal, and a noisereducing portion that executes noise reduction processing on theconverted signal based on the noise reduction degree.

With this image processing device, from the input signal and theconverted signal of the pixels it is possible to calculate a noisereduction degree that expresses a strength of the noise reductionprocessing to be adopted for each converted signal, and then adjust thestrength of the noise reduction processing based on the value of thenoise reduction degree that has been calculated. Thus, it is possible toperform noise reduction processing that corresponds to the degree towhich the noise component is amplified in each pixel due to grayscaleconversion.

A second aspect of the invention is the first aspect of the invention,in which the grayscale conversion portion performs grayscale conversionbased on histogram information for the grayscale value of the pixel dataof the input signal.

With this image processing device, it becomes possible to perform noisereduction processing that corresponds to the degree to which the noisecomponent is amplified in each pixel due to grayscale conversion, evenif histogram equalization is used as the method of grayscale conversion,for instance.

A third aspect of the invention is the first aspect of the invention, inwhich the grayscale conversion portion performs grayscale conversionbased on target pixel data of the input signal and a signal that isobtained by performing predetermined processing on surrounding pixeldata around the target pixel data.

With this image processing device, it is possible to effect a conversionwhile keeping noise from being enhanced, even in cases where the visualprocessing is executed on an input signal. Here, spatial-visualprocessing refers to processing in which grayscale correction isperformed on a target pixel (region) using grayscale characteristicsthat change according to the brightness of the area around the targetpixel (region). For instance, if the grayscale value of the target pixelin the input signal is small (such as an 8-bit grayscale value of 50)and the area surrounding the target pixel is dark (such as an 8-bitgrayscale value of 20), then grayscale correction is performed in orderto make the grayscale value of the target pixel a large grayscale value(such as an 8-bit grayscale value of 150). Conversely, if the grayscalevalue of the target pixel in the input signal is small (such as an 8-bitgrayscale value of 50) and the area surrounding the target pixel isbright (such as an 8-bit grayscale value of 100), then grayscalecorrection is performed in order to make the grayscale value of thetarget pixel a small grayscale value (such as an 8-bit grayscale valueof 30). This processing is one example of spatial-visual processing.

A fourth aspect of the invention is the first aspect of the invention,in which the noise reduction degree determining portion determines anoise reduction degree based on a ratio between the input signal and theconverted signal.

Thus, it is possible to adjust the strength of the noise reductionprocessing based on the gain that has been adopted for the input signal.

A fifth aspect of the invention is the first aspect of the invention, inwhich the noise reduction degree determining portion determines a noisereduction degree based on a ratio between the input signal and theconverted signal, and on the input signal.

Thus, it is possible to adjust the strength of the noise reductionprocessing based on the input signal and the gain that has been adoptedfor the input signal.

A sixth aspect of the invention is the fifth aspect of the invention, inwhich the noise reduction degree determining portion sets the noisereduction degree as a first noise reduction degree if a grayscaleconversion gain, which is the ratio of the converted signal to the inputsignal (=(the converted signal)/(the input signal)), is larger than afirst gain threshold, and the signal level of the input signal issmaller than a first signal level threshold. The noise reduction degreedetermining portion also sets the noise reduction degree as a secondnoise reduction degree, which is a smaller value than the first noisereduction degree, if the grayscale conversion gain is smaller than asecond gain threshold, which is a smaller value than the first gainthreshold, and the signal level of the input signal is smaller than thefirst signal level threshold. The noise reduction degree determiningportion also sets the noise reduction degree as a third noise reductiondegree, which is a smaller value than the first noise reduction degree,if the signal level of the input signal is larger than a second signallevel threshold, which is a larger value than the first signal levelthreshold.

With this image processing device, strong noise reduction processing isexecuted for pixel data whose input signal level (grayscale value of thepixel data of the input signal) is small and whose grayscale conversiongain is large, and weak noise reduction processing is executed for pixeldata whose input signal level is small and whose grayscale conversiongain is small. Further, weak noise reduction is performed for pixel datawhose input signal level is large.

Thus, even in cases where pixel data making up a dark region of an imagethat is formed by the input signal is amplified by a large gain tocreate an output signal, and that output signal is displayed on adisplay device, it is possible to improve the brightness or contrast ofthe region corresponding to those pixel data while suppressing the noisecomponent, on the display screen.

A seventh aspect of the invention is the fifth aspect of the invention,in which the noise reduction degree determining portion sets thegain-based noise reduction degree to a larger value the larger thegrayscale conversion gain, which is the ratio of the converted signal tothe input signal (=(the converted signal)/(the input signal)), and setsthe signal level-based noise reduction degree to a smaller value thelarger the signal level of the input signal. The noise reduction degreedetermining portion also determines a noise reduction degree based onthe gain-based noise reduction degree and the signal level-based noisereduction degree.

Thus, it is possible to perform noise reduction processing thatcorresponds to the grayscale conversion gain and the signal level of theinput signal.

It should be noted that here, “setting the gain-based noise reductiondegree to a larger value the larger the grayscale conversion gain”refers to a relationship in which, for example, the gain-based noisereduction degree increases monotonically with respect to the change inthe grayscale conversion gain. This includes not only a strict monotonicincrease but also includes a substantially monotonic increase (it canalso include ranges which in part are not monotonically increasing). Forexample, if the grayscale conversion gain is smaller than apredetermined value (this shall be called a “first gain value”), thenthe gain-based noise reduction degree is fixed at a predetermined value(this shall be called a “first gain-based noise reduction degree”), andif the grayscale conversion gain is equal to or greater than the firstgain value but is equal to or less than a second gain value (which isgreater than the first gain value), then the gain-based noise reductiondegree is set to a value that monotonically increases with respect tothe grayscale conversion gain. If the grayscale conversion gain islarger than the second gain value, then it is set to a value that islarger than the first gain-based noise reduction degree. Such a casealso is included.

As the method for determining the noise reduction degree with the noisereduction degree determining portion, it is also possible to use amethod in which the mean value (including the arithmetic mean and thegeometrical mean) of the gain-based noise reduction degree and thesignal level-based noise reduction degree is found and that mean valueis established as the noise reduction degree, and a method in which aweighted mean of the gain-based noise reduction degree and the signallevel-based noise reduction degree is found and that weighted mean isestablished as the noise reduction degree. It should be noted that here,the weighted mean is obtained by first finding the smaller of thegain-based noise reduction degree and the signal level-based noisereduction degree (this shall be called “value A” and the larger valueshall be called “value B”) and assigning a large weight to the value Aand a small weight to the value B, and then finding the average of thoseto obtain the weighted mean. For example, it is possible to find theweighted mean through (weighted mean)=((value A)×3+(value B))/4.

An eighth aspect of the invention is the seventh aspect of theinvention, in which the noise reduction degree determining portion setsthe smaller of the gain-based noise reduction degree and the signallevel-based noise reduction degree as the noise reduction degree.

Thus, even in cases where pixel data making up a dark region of an imagethat is formed by the input signal is amplified by a large gain tocreate an output signal, and that output signal is displayed on adisplay device, it is possible to improve the brightness or contrast ofthe region corresponding to those pixel data while suppressing the noisecomponent on the display screen.

A ninth aspect of the invention is the fifth aspect of the invention, inwhich the noise reduction degree determining portion includes a firstnoise reduction degree calculation portion, a second noise reductiondegree calculation portion, and a noise reduction degree output portion.The first noise reduction degree calculation portion outputs a signallevel-based noise reduction degree whose value is smaller than when thesignal level of the input signal is equal to or less than apredetermined signal level threshold, if the signal level of the inputsignal is larger than the predetermined signal level threshold. Thesecond noise reduction degree calculation portion outputs a gain-basednoise reduction degree whose value is greater than when the grayscaleconversion gain is equal to or less than a predetermined gain threshold,if the grayscale conversion gain, which is the ratio of the convertedsignal to the input signal (=(the converted signal)/(the input signal)),is larger than the predetermined gain threshold. The noise reductiondegree output portion sets a value that has been calculated based on thesignal level-based noise reduction degree and the gain-based noisereduction degree as the noise reduction degree.

Thus, it is possible to perform noise reduction processing thatcorresponds to the grayscale conversion gain and the signal level of theinput signal.

A tenth aspect of the invention is the ninth aspect of the invention, inwhich the noise reduction degree output portion sets the smaller of thesignal level-based noise reduction degree and the gain-based noisereduction degree as the noise reduction degree.

With this configuration, it is possible to execute strong noisereduction processing for pixel data whose input signal level (grayscalevalue of the pixel data of the input signal) is small and whosegrayscale conversion gain is large, and execute weak noise reductionprocessing for pixel data whose input signal level is small and whosegrayscale conversion gain is small. Further, weak noise reduction can beperformed for pixel data whose input signal level is large.

Thus, even in cases where pixel data making up a dark region of an imagethat is formed by the input signal is amplified by a large gain tocreate an output signal, and that output signal is displayed on adisplay device, it is possible to improve the brightness or contrast ofthe region corresponding to those pixel data while suppressing the noisecomponent on the display screen.

An eleventh aspect of the invention is an image processing device thatis provided with a gain calculation portion that calculates a gain forconversion of an input signal that is made from pixel data that form animage, a multiplication portion that multiplies the gain with the inputsignal to obtain a converted signal, a noise reduction degreedetermining portion that determines a noise reduction degree for theconverted signal based on the gain, and a noise reducing portion thatexecutes noise reduction processing on the converted signal based on thenoise reduction degree.

With this configuration, it is possible to calculate a noise reductiondegree that expresses a strength of the noise reduction processing to beadopted for each converted signal from the input signal and the gain ofthe pixels, and then adjust the strength of the noise reductionprocessing based on the value of the noise reduction degree that hasbeen calculated. Thus, it becomes possible to perform noise reductionprocessing that corresponds to the degree to which the noise componentis amplified in each pixel due to grayscale conversion.

A twelfth aspect of the invention is the eleventh aspect of theinvention, in which the noise reduction degree determining portiondetermines a noise reduction degree for the converted signal based onthe input signal as well.

A 13th aspect of the invention is the eleventh aspect of the invention,in which the gain calculation portion calculates the gain based onhistogram information for the grayscale values of the pixel data of theinput signal.

With this image processing device, it becomes possible to perform noisereduction processing that corresponds to the degree to which the noisecomponent is amplified in each pixel due to grayscale conversion, evenif histogram equalization is to be executed, for instance.

A 14th aspect of the invention is the eleventh aspect of the invention,in which the gain calculation portion calculates the gain based ontarget pixel data of the input signal and a signal that is obtained byperforming predetermined processing on surrounding pixel data around thetarget pixel data.

Thus, it becomes possible to perform noise reduction processing thatcorresponds to the degree to which the noise component of the pixels isamplified due to grayscale conversion, even in a case where visualprocessing is to be executed on the input signal.

A 15th aspect of the invention is the 12th aspect of the invention, inwhich the noise reduction degree determining portion sets the noisereduction degree as a first noise reduction degree if the gain of theconverted signal is larger than a first gain threshold, and the signallevel of the input signal is smaller than a first signal levelthreshold. The noise reduction degree determining portion also sets thenoise reduction degree as a second noise reduction degree, which is asmaller value than the first noise reduction degree, if the gain issmaller than a second gain threshold, which is a smaller value than thefirst gain threshold, and the signal level of the input signal issmaller than the first signal level threshold. The noise reductiondegree determining portion also sets the noise reduction degree as athird noise reduction degree, which is a smaller value than the firstnoise reduction degree, if the signal level of the input signal islarger than a second signal level threshold, which is a larger valuethan the first signal level threshold.

With this image processing device, strong noise reduction processing isexecuted for pixel data whose input signal level (grayscale value of thepixel data of the input signal) is small and whose signal gain forconversion is large, and weak noise reduction processing is executed forpixel data whose input signal level is small and whose signal gain forconversion is small. Weak noise reduction is also performed for pixeldata whose input signal level is large.

Thus, even in cases where pixel data making up a dark region of an imagethat is formed by the input signal are amplified by a large gain tocreate an output signal, and that output signal is displayed on adisplay device, it is possible to improve the brightness or contrast ofthe region corresponding to those pixel data while suppressing the noisecomponent on the display screen.

A 16th aspect of the invention is the 12th aspect of the invention, inwhich the noise reduction degree determining portion sets a gain-basednoise reduction degree to a larger value the larger gain, sets a signallevel-based noise reduction degree to a smaller value the larger thesignal level of the input signal, and determines the noise reductiondegree based on the gain-based noise reduction degree and the signallevel-based noise reduction degree.

Thus, it is possible to perform noise reduction processing thatcorresponds to the grayscale conversion gain and the signal level of theinput signal.

It should be noted that here, “setting the gain-based noise reductiondegree to a larger value the larger the gain” refers to a relationshipin which, for example, the gain-based noise reduction degree increasesmonotonically with respect to the change in the gain. This includes notonly a monotonic increase in a strict sense but also includes asubstantially monotonic increase (it can also include ranges which inpart are not monotonically increasing). For example, if the gain issmaller than a predetermined value (this shall be called a “third gainvalue”), then the gain-based noise reduction degree is fixed at apredetermined value (this shall be called a “third gain-based noisereduction degree”), and if the gain is equal to or greater than thethird gain value but is equal to or less than a fourth gain value(greater than the third gain value), then the gain-based noise reductiondegree is set to a value that monotonically increases with respect tothe gain. If the gain is larger than the fourth gain value, then it isset to a value that is larger than the third gain-based noise reductiondegree. Such a case also is included.

As the method for determining the noise reduction degree with the noisereduction degree determining portion, it is also possible to use amethod in which the mean value (including the arithmetic mean and thegeometrical mean) of the gain-based noise reduction degree and thesignal level-based noise reduction degree is found and that mean valueis established as the noise reduction degree, and a method in which aweighted mean of the gain-based noise reduction degree and the signallevel-based noise reduction degree is found and that weighted mean isestablished as the noise reduction degree.

A 17th aspect of the invention is the 16th aspect of the invention, inwhich the noise reduction degree determining portion sets the smaller ofthe gain-based noise reduction degree and the signal level-based noisereduction degree as the noise reduction degree.

Thus, even in cases where pixel data making up a dark region of an imagethat is formed by the input signal are amplified by a large gain tocreate an output signal, and that output signal is displayed on adisplay device, it is possible to improve the brightness or contrast ofthe region corresponding to those pixel data while suppressing the noisecomponent on the display screen.

An 18th aspect of the invention is the 12th aspect of the invention, inwhich the noise reduction degree determining portion includes a firstnoise reduction degree calculation portion, a second noise reductiondegree calculation portion, and a noise reduction degree output portion.The first noise reduction degree calculation portion outputs a signallevel-based noise reduction degree whose value is smaller than when thesignal level of the input signal is equal to or less than apredetermined signal level threshold, if the signal level of the inputsignal is larger than the predetermined signal level threshold. Thesecond noise reduction degree calculation portion outputs a gain-basednoise reduction degree whose value is greater than when the gain isequal to or less than a predetermined gain threshold, if the gain islarger than the predetermined gain threshold. The noise reduction degreeoutput portion sets a value that has been calculated based on the signallevel-based noise reduction degree and the gain-based noise reductiondegree as the noise reduction degree.

Thus, it is possible to perform noise reduction processing thatcorresponds to the grayscale conversion gain and the signal level of theinput signal.

A 19th aspect of the invention is the 18th aspect of the invention, inwhich the noise reduction degree output portion sets the smaller of thesignal level-based noise reduction degree and the gain-based noisereduction degree as the noise reduction degree.

With this image processing device, strong noise reduction processing isexecuted for pixel data whose input signal level (grayscale value of thepixel data of the input signal) is small and whose signal gain forconversion is large, and weak noise reduction processing is executed forpixel data whose input signal level is small and whose signal gain forconversion is small. Weak noise reduction is also performed for pixeldata whose input signal level is large.

Thus, even in a case where pixel data making up a dark region of animage that is formed by the input signal are amplified by a large gainto create an output signal, and that output signal is displayed on adisplay device, it is possible to improve the brightness or contrast ofthe region corresponding to those pixel data while suppressing the noisecomponent on the display screen.

A 20th aspect of the invention is an image processing method thatincludes a grayscale conversion step of performing grayscale conversionon an input signal that is made from pixel data that form an image toobtain a converted signal, a noise reduction degree determining step ofdetermining a noise reduction degree for the converted signal based onthe input signal and the converted signal, and a noise reducing step ofexecuting noise reduction processing on the converted signal based onthe noise reduction degree.

With this configuration, from the input signal and the converted signalof each pixel it is possible to calculate a noise reduction degree thatexpresses a strength of the noise reduction processing to be adopted foreach converted signal, and then adjust the strength of the noisereduction processing based on the value of the noise reduction degreethat has been calculated. Thus, it becomes possible to perform noisereduction processing that corresponds to the degree to which the noisecomponent is amplified in each pixel due to grayscale conversion.

A 21st aspect of the invention is an image processing method thatincludes a gain calculation step of calculating a gain for converting aninput signal that is made from pixel data that form an image, amultiplication step of multiplying the gain with the input signal toobtain a converted signal, a noise reduction degree determining step ofdetermining a noise reduction degree for the converted signal based onthe gain, and a noise reducing step of executing noise reductionprocessing on the converted signal based on the noise reduction degree.

With this configuration, it is possible to calculate a noise reductiondegree that expresses a strength of the noise reduction processing to beadopted for each converted signal from the input signal and the gain ofthe pixels, and then adjust the strength of the noise reductionprocessing based on this value. Thus, it becomes possible to performnoise reduction processing that corresponds to the degree to which thenoise component is amplified in each pixel due to grayscale conversion.

A 22nd aspect of the invention is the 21st aspect of the invention, inwhich in the noise reduction degree determining step, a noise reductiondegree for the converted signal is determined based on the input signalas well.

A 23rd aspect of the invention is an image processing program forcausing a computer to execute a grayscale conversion step of performinggrayscale conversion on an input signal that is made from pixel datathat form an image to obtain a converted signal, a noise reductiondegree determining step of determining a noise reduction degree for theconverted signal based on the input signal and the converted signal, anda noise reducing step of executing noise reduction processing on theconverted signal based on the noise reduction degree.

With this configuration, from the input signal and the converted signalof the pixels it is possible to calculate a noise reduction degree thatexpresses a strength of the noise reduction processing to be adopted foreach converted signal, and then adjust the strength of the noisereduction processing based on that value. Thus, it becomes possible toperform noise reduction processing that corresponds to the degree towhich the noise component is amplified in each pixel due to grayscaleconversion.

A 24th aspect of the invention is an image processing program forcausing a computer to execute a gain calculation step of calculating again for converting an input signal that is made from pixel data thatform an image, a multiplication step of multiplying the gain with theinput signal to obtain a converted signal, a noise reduction degreedetermining step of determining a noise reduction degree for theconverted signal based on the gain, and a noise reducing step ofexecuting noise reduction processing on the converted signal based onthe noise reduction degree.

With this configuration, it is possible to calculate a noise reductiondegree that expresses a strength of the noise reduction processing to beadopted for each converted signal from the input signal and the gain ofthe pixels, and then adjust the strength of the noise reductionprocessing based on the value of the noise reduction degree that hasbeen calculated. Thus, it becomes possible to perform noise reductionprocessing that corresponds to the degree to which the noise componentis amplified in each pixel due to grayscale conversion.

A 25th aspect of the invention is the 24th aspect of the invention, inwhich in the noise reduction degree determining step, a noise reductiondegree for the converted signal is determined based on the input signalas well.

A 26th aspect of the invention is an integrated circuit that is providedwith a grayscale conversion portion that performs grayscale conversionon an input signal that is made from pixel data that form an image toobtain a converted signal, a noise reduction degree determining portionthat determines a noise reduction degree for the converted signal basedon the input signal and the converted signal, and a noise reducingportion that executes noise reduction processing on the converted signalbased on the noise reduction degree.

Thus, it is possible to achieve an integrated circuit that achieves thesame effects as the first aspect of the invention.

A 27th aspect of the invention is an integrated circuit that includes again calculation portion that calculates a gain for conversion of aninput signal that is made from pixel data that form an image, amultiplication portion that multiplies the gain with the input signal toobtain a converted signal, a noise reduction degree determining portionthat determines a noise reduction degree for the converted signal basedon the gain, and a noise reducing portion that executes noise reductionprocessing on the converted signal based on the noise reduction degree.

Thus, it is possible to achieve an integrated circuit that achieves thesame effects as the eleventh aspect of the invention.

With this invention, it is possible to provide an image processingdevice, an image processing method, an image processing program, and anintegrated circuit with which it is possible to execute noise reductionprocessing at a different strength for each target pixel, in accordancewith the degree of the deterioration of the S/N ratio due toamplification of noise components, not only in the case of grayscaleconversion in which only the target pixel is referenced, but also in thecase of grayscale conversion in which the region surrounding the targetpixel is also referenced.

Consequently, with the invention it is possible to convert the grayscaleof an input signal without amplifying noise components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the image processing device according to afirst embodiment of the invention.

FIG. 2 is a block diagram of the grayscale conversion portion of thefirst embodiment.

FIG. 3 is a block diagram of the noise reduction degree determiningportion of the first embodiment.

FIG. 4 is a diagram that shows the relationship between the input signaland the first noise reduction degree in the first noise reduction degreecalculation portion of the first embodiment.

FIG. 5 is a diagram that shows the relationship between the ratiobetween the converted signal and the input signal and the second noisereduction degree in the second noise reduction degree calculationportion of the first embodiment.

FIG. 6 is a block diagram of the noise reducing portion of the firstembodiment.

FIG. 7 is a block diagram of a modified example of the grayscaleconversion portion of the first embodiment.

FIG. 8 is a diagram that shows the relationship between the input signaland the converted signal in the visual processing in the firstembodiment.

FIG. 9 is a block diagram of an image processing device according to thesecond embodiment of the invention.

FIG. 10 is a block diagram of the gain calculation portion of the secondembodiment.

FIG. 11 is a block diagram of a modified example of the gain calculationportion of the second embodiment.

FIG. 12 is a diagram that shows the relationship between the inputsignal and the gain in the visual processing in the second embodiment.

FIG. 13 is a block diagram of the noise reduction degree determiningportion of the second embodiment.

FIG. 14 is a diagram that shows the relationship between the gain andthe second noise reduction degree in the second embodiment.

EXPLANATION OF THE REFERENCE NUMERALS

-   1000, 2000 image processing device-   100, 150 grayscale conversion portion-   101 histogram calculation portion-   102 input/output characteristics determining portion-   103 grayscale processing portion-   110, 160 gain calculation portion-   112 grayscale conversion gain characteristics determining portion-   113 grayscale processing gain calculation portion-   120 multiplication portion-   151 spatial processing portion-   152 visual processing portion-   162 visual processing gain calculation portion-   200, 210 noise reduction degree determining portion-   201 first noise reduction degree calculation portion-   202 division portion-   203, 213 second noise reduction degree calculation portion-   204 minimum value output portion-   300 noise reducing portion-   301 smoothing portion-   302 interpolation portion

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Below, embodiments of the invention are described in detail withreference to the drawings.

First Embodiment

An image processing device according to a first embodiment of theinvention is described using FIGS. 1 through 3.

FIG. 1 is a block diagram that shows the configuration of an imageprocessing device 1000 according to the first embodiment of theinvention.

As shown in FIG. 1, the image processing device 1000 is provided with agrayscale conversion portion 100 for calculating a converted signal TSfrom an input signal IS, a noise reduction degree determining portion200 for determining a noise reduction degree NR based on the inputsignal IS and the converted signal TS, and a noise reducing portion 300that performs noise reduction processing on the converted signal TSbased on the noise reduction degree NR to obtain an output signal OS.

With this configuration, the noise reduction degree determining portion200 determines the noise reduction degree to be applied to each pixelbased on the degree that the noise component is amplified by thegrayscale conversion portion 100, and the noise reducing portion 300 canexecute noise reduction based on the noise reduction degree NR that hasbeen calculated by the noise reduction degree determining portion 200.Thus, an image that has preferable grayscale characteristics can bereproduced without amplifying the noise component when converting thegrayscale of an input signal.

The various functional portions of FIG. 1 are described below.

The grayscale conversion portion 100 inputs an input signal IS andperforms grayscale conversion on the input signal IS to correct thebrightness or contrast, creating a converted signal TS, and outputs theconverted signal TS to the noise reduction degree determining portion200 and the noise reducing portion 300. It is possible to adopthistogram equalization for the grayscale conversion by the grayscaleconversion portion 100, in which grayscale conversion of the inputsignal IS is performed based on the histogram information of the entireimage. FIG. 2 is a block diagram showing the configuration of agrayscale conversion portion 100 that executes grayscale conversionthrough histogram equalization.

As shown in FIG. 2, first a histogram calculation portion 101 calculateshistogram information HG from the input signal IS and outputs thehistogram information HG to an input/output characteristics determiningportion 102. Next, the input/output characteristics determining portion102 inputs the histogram information HG, and based on the histograminformation HG that has been calculated by the histogram calculationportion 101, determines the input/output characteristics HC such that awide range of grayscale is assigned to the grayscale levels (grayscalevalues) that appear with high frequency. The input/outputcharacteristics determining portion 102 then outputs the data making upthe input/output characteristics HC to a grayscale processing portion103.

Here, the grayscale processing portion 103 inputs the data making up theinput/output characteristics HC and converts the input signal IS of atarget pixel based on the input/output characteristics HC that have beencalculated by the input/output characteristics determining portion 102,yielding a converted signal TS. The grayscale processing portion 103outputs the converted signal TS that has been obtained to the noisereducing portion 300 and the noise reduction degree determining portion200.

It should be noted that the method of the grayscale conversion is notlimited to a method that is achieved by the grayscale conversion portion100 shown in FIG. 2. Further, the configuration of the grayscaleconversion portion 100 is not limited to the configuration that is shownin FIG. 2.

Modified Example (Spatial-Visual Processing)

A modified example using a grayscale conversion method that is separatefrom the grayscale conversion method discussed above is described as thegrayscale conversion method.

The grayscale conversion method according to the modified example shownhere is a visual processing method in which grayscale conversion isperformed based on the target pixel of the input signal and a signalobtained by performing a predetermined processing on the pixelssurrounding the target pixel.

FIG. 7 is a block diagram showing the configuration of a grayscaleconversion portion 150 that performs grayscale conversion through visualprocessing according to the modified example. As shown in FIG. 7, thegrayscale conversion portion 150 is provided with a spatial processingportion 151 that extracts a surrounding image signal US that includesthe surrounding image information from the input signal IS, and a visualprocessing portion 152 that calculates a converted signal TS that isobtained by visually processing the input signal IS according to thesurrounding image signal US.

Here, the spatial processing portion 151 performs filter processing onthe target pixel and the surrounding pixels of the input signal IS. Thespatial processing portion 151 for example calculates the surroundingimage signal US by executing the following low-pass filter on the targetpixel and the surrounding pixels of the input signal IS.

US=(Σ[Wij]×[Aij])÷(Σ[Wij])

Here, [Wij] is the weight coefficient of the pixel located in the i-throw j-th column of a matrix of the target pixel and surrounding pixels,and [Aij] is the pixel value of the pixel that is located in the i-throw j-th column of a matrix of the target pixel and surrounding pixels.The symbol Σ means to take the sum of the values for the target pixeland the surrounding pixels (calculate the sum of the series).

It should be noted that it is possible to assign a weight coefficientwith a smaller value the larger the absolute value of the differencebetween the pixel values, and it is also possible to assign a smallerweight coefficient the greater the distance from the target pixel.

Next, the visual processing portion 152 inputs the input signal IS andthe surrounding image signal US, which is the output from the spatialprocessing portion, and creates and outputs a converted signal TS byconverting the grayscale of the input signal IS in accordance with thesurrounding image signal US. The visual processing portion 152 forexample can perform grayscale conversion based on the two-dimensionalgrayscale conversion characteristics shown in FIG. 8. Here,two-dimensional grayscale conversion refers to grayscale conversion inwhich the value of the output is determined for both the surroundingimage signal US and the input signal IS.

FIG. 8 shows a graph of the two-dimensional grayscale conversioncharacteristics. In FIG. 8, the horizontal axis is the pixel value(grayscale value) of the input signal IS, and the vertical axis is thepixel value (grayscale value) of the converted signal TS. It should benoted that in FIG. 8, the input signal and the converted signal TS are8-bit signals whose pixel values (grayscale values) are in the range of0 to 255.

As shown in FIG. 8, two-dimensional grayscale conversion haspredetermined two-dimensional grayscale conversion characteristics thatdepend on the signal level (grayscale value) of the surrounding imagesignal US, or on USO to USn (where n is an integer that correlates withthe signal level (grayscale value)). That is, two-dimensional grayscaleconversion is achieved by converting the input signal IS (the grayscalevalue of IS) into the converted signal TS (the grayscale value of TS)with a grayscale conversion curve that has been selected from among thegrayscale conversion curves USO to USn based on the signal level(grayscale level) of the surrounding image signal US. For example, thegrayscale conversion curve US1 of FIG. 8 is selected when the level(grayscale value) of the US signal is 1, and the grayscale conversioncurve US120 is selected when the level (grayscale value) of the USsignal is 120. However, it is not absolutely necessary to prepare thesame number of gain conversion curves USO to USn as the number ofgrayscale values of the US signal, and it is also possible to forexample prepare a smaller number of gain conversion curves USO to USnthan the number of grayscale values of the US signal, and for the gainconversion curves that have not been readied, to achieve two-dimensionalgain conversion by calculating a grayscale conversion curve thatcorresponds to the grayscale value of the US signal throughinterpolation of the gain conversion curves that have been prepared.

For example, if the surrounding image signal US is an 8-bit value, thenin two-dimensional grayscale conversion, the grayscale conversioncharacteristics are separated into 256 levels and each of these isexpressed as a grayscale conversion curve that has predetermined gammaconversion characteristics.

As shown in FIG. 8, the grayscale conversion characteristics areexpressed by a plurality of grayscale conversion curves havingpredetermined gamma conversion characteristics, and the plurality ofgrayscale conversion curves have a relationship where the outputmonotonically decreases along with the subscript of the surroundingimage signal US (if the grayscale value of the input signal is the samevalue, then the grayscale value that is output becomes a smaller valuethe larger the subscript (number) of the surrounding image signal US).It should be noted that the “relationship where the output monotonicallydecreases” is not limited to a monotonically decreasing relationship ina strict sense, and even if there are places where the output partiallydoes not monotonically decrease along with the subscript of thesurrounding image signal US, it is sufficient for it to be substantiallymonotonically decreasing.

As shown in FIG. 8, the two-dimensional grayscale conversioncharacteristics satisfy the relationship of (the output value whenUS=USO)≧(the output value when US=US1)≧ . . . ≧(the output value whenUS=USn) with respect to the grayscale value of the pixel of all inputsignals IS. The contrast of the local region is enhanced by thisgrayscale conversion characteristic.

With the two-dimensional grayscale conversion characteristics shown inFIG. 8, when the image signal IS value (grayscale value) is “a,” thevisual processing portion 152 can take a value from “P” to “Q” for thegrayscale value of the converted signal TS, based on the surroundingimage signal US. That is, even if the input signal IS value (grayscalevalue) is “a,” the value (grayscale value) of the converted signal TSthat is output may vary significantly between the value “P” and thevalue “Q” depending on the surrounding image signal US.

As described above, the grayscale conversion portion 150 of the modifiedexample makes it possible to execute grayscale conversion thatcorresponds to the surrounding image information for each pixel in theimage.

Next, the noise reduction degree determining portion 200 inputs theinput signal IS and the converted signal TS, and from the input signalIS and the converted signal TS calculates a noise reduction degree NRthat expresses the strength of the noise reduction processing to beexecuted on the converted signal TS. The noise reduction degreedetermining portion 200 then outputs the noise reduction degree NR thatit has calculated to the noise reducing portion 300. In general, noisecomponents are amplified more significantly the smaller the signal level(grayscale value) of the input signal IS for the pixel and the largerthe gain value that has been used to convert the grayscale of the pixel.Thus, it is preferable to execute stronger noise reduction processingthe smaller the signal level (grayscale value) of the input signal ISand the larger the gain of the grayscale conversion, that is, the ratiobetween the converted signal TS and the input signal IS(=(grayscalevalue of converted signal TS)/(grayscale value of input signal IS)).

Hereinafter, the noise reduction degree NR is described as taking an(real) value from 0 to 1, in which the larger the value, the strongerthe noise reduction processing that is performed.

FIG. 3 shows a block diagram of the noise reduction degree determiningportion 200 for performing processing to calculate the noise reductiondegree NR.

As shown in FIG. 3, the noise reduction degree determining portion 200is primarily made of a first noise reduction degree calculation portion201 for calculating a first noise reduction degree NRA from the inputsignal IS, a division portion 202 for calculating a ratio DIV betweenthe converted signal TS and the input signal IS (=(grayscale value ofTS)/(grayscale value of IS)), a second noise reduction degreecalculation portion 203 for calculating a second noise reduction degreeNRB from the value of the ratio DIV that has been calculated, and aminimum value output portion 204 that serves as the noise reductiondegree determining portion and outputs the smaller of the first noisereduction degree NRA and the second noise reduction degree NRB.

Here, it is desirable for the first noise reduction degree calculationportion 201 to execute stronger noise reduction processing the smallerthe signal level (grayscale value) of the pixel, and thus it preferablyoutputs a larger noise reduction degree NRA the smaller the signal level(grayscale value) of the input signal IS. FIG. 4 shows an example ofthese properties.

On the other hand, because it is desirable for the second noisereduction degree calculation portion 203 to execute stronger noisereduction the higher the gain that has been used to convert thegrayscale of the pixel, preferably it outputs a larger noise reductiondegree NRB the larger the ratio DIV between the converted signal TS andthe input signal IS. FIG. 5 shows an example of these properties.

Next, the minimum value output portion 204 outputs the smaller of thefirst noise reduction degree NRA that has been calculated by the firstnoise reduction degree calculation portion 201 and the second noisereduction degree NRB that has been calculated by the second noisereduction degree calculation portion 203, and thus it is possible tocalculate a larger noise reduction degree NR the smaller the signallevel (grayscale level) of the input signal IS for the pixel and thelarger the gain that has been used to convert the grayscale of thepixel. The noise reduction degree determining portion 200 outputs thenoise reduction degree NR that has been calculated to the noise reducingportion 300.

It should be noted that the processing by the minimum value outputportion 204 is not limited to the processing described above, and forexample it may also be processing in which the mean value (including thearithmetic mean and the geometrical mean) of the first noise reductiondegree NRA and the second noise reduction degree NRB is found and thatmean value is taken as the noise reduction degree NR, and processing inwhich the weighted mean of the first noise reduction degree NRA and thesecond noise reduction degree NRB is found and that weighted mean istaken as the noise reduction degree NR. Here, the weighted mean isobtained by first finding the smaller of the two values for the firstnoise reduction degree NRA and the second noise reduction degree NRB(this smaller value shall be called “value A” and the larger value shallbe called “value B”) and assigning a large weight to the value A and asmall weight to the value B, and then averaging these to obtain theweighted mean. For example, it is possible to find the weighted meanthrough the equation (weighted mean)=((value A)×3+(value B))/4.

Next, the noise reducing portion 300 performs noise reduction processingon the converted signal TS based on the noise reduction degree NR. FIG.6 shows an example of the configuration of the noise reducing portion300 for adjusting the strength of the noise reduction processing basedon the noise reduction degree NR.

As shown in FIG. 6, the noise reducing portion 300 is primarily made ofa smoothing portion 301 for smoothing the converted signal TS to obtaina smoothed signal LPS, and an interpolation portion 302 forinterpolating the converted signal TS and the smoothed signal LPS basedon the noise reduction degree NR in order to obtain an output signal OS.

Here, the smoothing portion 301 can be achieved by applying a low-passfilter, for example, to a plurality of pixels surrounding a targetpixel.

The interpolation by the interpolation portion 302 can be achievedthrough the interpolation of the following equation, using the noisereduction degree NR that has been calculated by the noise reductiondegree determining portion 200.

OS=LPS×NR+TS×(1−NR)

According to this equation, when the noise reduction degree NR is large,the resulting output is a value in which the smoothed signal LPS isweighted heavily, yielding a result (output) in which the noise has beenstrongly eliminated. Conversely, when the noise reduction degree NR issmall, the resulting output is a value in which the converted signal TSis weighted heavily, yielding a result (output) in which the noise hasbeen weakly eliminated.

Noise tends to stand out on the display screen in cases where an outputsignal OS that is obtained by applying a large gain to an input signalIS that corresponds to a target pixel with a small grayscale value (sucha target pixel is displayed dark on the display screen) is displayed ona display device.

In other words, an input signal IS that corresponds to a target pixelwith a small grayscale value has a small signal level (grayscale value)for the target pixel, and thus assuming an equal noise component, theS/N ratio is poor compared to a case in which the signal level(grayscale value) of the target pixel is large. Thus, when an outputsignal OS that has been obtained by amplifying this signal with a poorS/N ratio is displayed on a display device, the display has a poor S/Nratio and noise stands out easily.

Even if processing that causes blurring (such as processing in which LPFis applied) is executed on a target pixel with a small grayscale value,it is difficult to sense a deterioration in terms of the visualcharacteristics on the display screen.

Thus, it is preferable to perform strong noise reduction on regions thatcorrespond to pixels with a small grayscale value, and on regions thathave been amplified with a large gain.

On the other hand, an input signal IS that corresponds to a target pixelwith a large grayscale value has a large signal level (grayscale value)for the target pixel, and thus assuming an equal noise component, theS/N ratio is good compared to a case in which the signal level(grayscale value) of the target pixel is small. Thus, if this isamplified by a large gain, noise does not stand out easily on thedisplay screen.

Conversely, when processing that causes blurring (such as processing inwhich LPF is applied) is executed on a target pixel with a largegrayscale value, it is easy to sense a deterioration in the visualcharacteristics on the display screen.

Thus, it is preferable that weak noise reduction is performed on regionsthat correspond to pixels with a large grayscale value.

The characteristics shown in FIG. 4, the characteristics shown in FIG.5, and the configuration of the noise reduction degree determiningportion 200 are determined based on the above principles. It should benoted that it goes without saying that the characteristics shown in FIG.4, the characteristics shown in FIG. 5, and the configuration of thenoise reduction degree determining portion 200 can be altered as long asthey are altered based on the above principles.

As described above, with the image processing device 1000 according tothis embodiment, the degree to which the noise component of each pixelis amplified can be found from the signal level (grayscale value) of theinput signal IS and the value of the gain if the input signal IS isgrayscale converted to the converted signal TS, and thus by performingnoise reduction at a suitable strength based on the degree to which thenoise component will be amplified, it is possible to obtain an outputsignal OS whose noise component has been suppressed and which haspreferable grayscale characteristics. With the image processing device1000 according to the embodiment, it is possible to execute preferablegrayscale conversion on an input signal IS without amplifying the noisecomponent of the input signal IS, and output the result as the outputsignal OS, and thus by displaying the output signal OS that is outputfrom the image processing device 1000 according to this embodiment on adisplay device (not shown) as an image (video picture), it is possibleto reproduce an image (video picture) that has preferable grayscalecharacteristics.

It should be noted that it is also possible for the noise reductiondegree NR to be calculated from only the ratio DIV of the convertedsignal TS and the input signal IS. Thus, it is possible to executestrong noise reduction on all pixels that have been processed with ahigh gain, regardless of the value of the input signal IS.

It is also possible for the noise reduction degree NR to be calculatedbased on the difference between the converted signal TS and the inputsignal IS. Thus, it is possible to execute strong noise reduction onpixels in which the converted signal TS is significantly higher than theinput signal IS.

It should be noted that that the strength of the noise reductionprocessing by the noise reducing portion 300 can be adjusted by changingthe filter coefficient of the low-pass filter that is applied to theconverted signal TS. Thus, it is possible to adjust the degree to whichhigh-frequency components are reduced by the low-pass filter, and thedegree to which the noise is reduced can be adjusted.

Second Embodiment

An image processing device according to a second embodiment of theinvention is described using FIGS. 9 through 14.

In the image processing device 1000 according to the first embodiment ofthe invention, once a converted signal TS has been calculated from theinput signal IS by the grayscale conversion portion 100, the noisereduction degree determining portion 200 calculates the noise reductiondegree NR from the input signal IS and the converted signal TS. With theimage processing device 2000 according to the second embodiment of theinvention, once a gain GN for grayscale conversion has been calculatedfrom the input signal IS, the gain GN and the input signal IS aremultiplied to calculate a converted signal TS, and the noise reductiondegree NR is calculated from the input signal IS and the gain GN. Thisembodiment is described below using FIG. 9.

FIG. 9 is a block diagram that shows the configuration of the imageprocessing device 2000 according to the second embodiment of theinvention. Hereinafter, portions that are identical to those of thefirst embodiment have been assigned the same reference numerals asbefore and will not be described in detail.

In FIG. 9, the image processing device 2000 according to the secondembodiment of the invention is provided with a gain calculation portion110 for outputting a gain GN from an input signal IS, a multiplicationportion 120 for calculating a converted signal TS based on the inputsignal IS and the gain GN, a noise reduction degree determining portion210 for calculating a noise reduction degree NR based on the inputsignal IS and the gain GN, and a noise reducing portion 300 forperforming noise reduction processing on the converted signal TS basedon the noise reduction degree NR to obtain an output signal OS.

When this configuration is used, the noise reduction degree determiningportion 210 can specify pixels whose noise component has been amplifiedsignificantly when converted from the input signal IS to the convertedsignal TS, and the noise reducing portion 300 can execute suitable noisereduction processing based on the noise reduction degree NR that hasbeen calculated by the noise reduction degree determining portion 210.

Thus, with the image processing device 2000 of this embodiment, it ispossible to suitably execute noise reduction processing even on pixelswhose noise component is amplified significantly when grayscaleconversion is performed by multiplying the input signal and the gain,and thus by displaying the output signal OS that has been processed andoutput by the image processing device 2000 of this embodiment as animage (video picture) on a display device (not shown), it is possible toreproduce an image (video picture) that has preferable grayscalecharacteristics without amplifying noise components in the image.

Below, the functional portions in FIG. 9 that are different from thoseportions in the first embodiment are described.

The gain calculation portion 110 calculates the gain GN, which is thegain value for correcting the brightness or contrast of the input signalIS. The brightness or the contrast of the input signal IS is correctedby multiplying the gain GN and the input signal IS.

To achieve the gain calculation portion 110, it is for example possibleto adopt an approach that is based on histogram equalization, in whichthe grayscale of the input signal IS is transformed based on thehistogram information. FIG. 10 is a block diagram that shows aconfiguration of the gain calculation portion 110 that is based onhistogram equalization.

As shown in FIG. 10, the gain calculation portion 110 primarily includesa histogram calculation portion 101 for calculating histograminformation HG from an input signal IS, a grayscale conversion gaincharacteristics determining portion 112 for determining the gain outputcharacteristics GC from the histogram information HG, and a grayscaleprocessing gain calculation portion 113 for calculating the gain GNbased on the gain output characteristics GC and the input signal IS.

In FIG. 10, like in the histogram equalization described in the firstembodiment, first the histogram calculation portion 101 calculates thehistogram information HG from the input signal IS.

Next, the grayscale conversion gain characteristics determining portion112 determines the gain output characteristics GC, which have been setso that a wide range of grayscale is allocated the more frequently agrayscale level (grayscale value) appears in the input signal IS basedon the histogram information HG, for each target pixel of the inputsignal IS, for each predetermined block (region) made of a plurality ofpixels that includes the target pixel, or for the entire image.

The grayscale processing gain calculation portion 113 outputs a gain GNfor multiplication with the input signal IS of the target pixel, to themultiplication portion 120 and the noise reduction degree determiningportion 210, based on the gain output characteristics GC that have beencalculated by the grayscale conversion gain characteristics determiningportion 112.

It should be noted that the method for calculating the gain is notlimited to the method that is achieved by the gain calculation portion110 shown in FIG. 10. Further, the configuration of the gain calculationportion 110 is not limited to the configuration that is shown in FIG.10.

Modified Example (Gain-Type Spatial-Visual Processing)

A modified example using a gain calculation method that is separate fromthe gain calculation method discussed above is described as a method forcalculating the gain using grayscale conversion (gain calculationmethod).

The method of grayscale conversion (grayscale conversion using a gain)according to the modified example shown here is a method based on visualprocessing in which a gain is calculated based on the target pixel ofthe input signal IS and a signal obtained by performing predeterminingprocessing on the pixels surrounding the target pixel.

FIG. 11 is a block diagram that shows the configuration of a gaincalculation portion 160 for performing grayscale conversion by visualprocessing according to this modified example. As shown in FIG. 11, thegain calculation portion 160 is provided with a spatial processingportion 151 that extracts a surrounding image signal US that includesthe surrounding image information from the input signal IS, and a visualprocessing gain calculation portion 162 that calculates a gain GN forvisual processing of the input signal IS according to the surroundingimage signal US.

The processing by the spatial processing portion 151 is the same as inthe first embodiment, which was described in FIG. 7, and thus here willnot be described in detail.

Next, the visual processing gain calculation portion 162 calculates again GN to be multiplied with the input signal IS according to the inputsignal IS and the surrounding image signal US. Like in the modifiedexample of the first embodiment, the processing in this case also istwo-dimensional grayscale conversion, because the gain GN for convertingthe grayscale of the input signal IS is determined based on the twoinputs of the surrounding image signal US and the input signal IS. Here,in order to achieve the same grayscale conversion characteristics as thetwo-dimensional grayscale conversion characteristics shown in FIG. 8, itis also possible to substitute the output value of the dimensionalgrayscale conversion characteristics with the ratio of the convertedsignal TS and the input signal IS (that is, the gain GN) shown in FIG.8. These input/output characteristics are shown in FIG. 12.

As shown in FIG. 12, the processing of outputting the gain GN haspredetermined gain GN output characteristics that depend on the signallevel (grayscale value) of the surrounding image signal US and on USO toUSn (where n is an integer corresponding to the signal level (grayscalevalue)). In other words, two-dimensional gain conversion is achieved byselecting any one of the gain conversion curves USO to USn according tothe signal level (grayscale level) of the surrounding image signal US,and then converting the input signal IS (grayscale value of the IS) to again GN based on the gain conversion curve that has been selected. Forexample, the curve US1 of FIG. 12 is selected when the level (grayscalevalue) of the US signal is 1, and the curve US120 is selected when thelevel (grayscale value) of the US signal is 120. However, it is notabsolutely necessary to prepare the same number of gain conversioncurves USO to USn as the number of grayscale values of the US signal,and it is also possible to for example prepare a smaller number of gainconversion curves USO to USn than the number of grayscale values of theUS signal, and for the gain conversion curves that are not readied, toachieve two-dimensional gain conversion by calculating a grayscaleconversion curve that corresponds to the grayscale value of the USsignal through interpolation of the gain conversion curves that havebeen prepared.

In two-dimensional gain conversion, for example, when the surroundingimage signal US is an 8-bit value, the gain conversion characteristicsare separated into 256 levels and these can be expressed as conversioncurves each having predetermined gain GN output characteristics (gainconversion characteristics).

As shown in FIG. 12, the gain conversion characteristics that arecorresponded with the grayscale conversion characteristics are expressedas a plurality of gain conversion curves (curves for converting thegrayscale value to a gain value) that have gain conversioncharacteristics that correspond to predetermined gamma conversioncharacteristics, and the plurality of gain conversion curves have therelationship where the output monotonically decreases along with thesubscript of the surrounding image signal US (if the grayscale value ofthe input signal is the same value, then the gain value that is outputbecomes a smaller value the larger the subscript (number) of thesurrounding image signal US). It should be noted that the “relationshipwhere the output monotonically decreases” is not strictly limited to amonotonically decreasing relationship, and even if there are placeswhere the output partially does not monotonically decrease for thesubscript of the surrounding image signal US, it is sufficient for it tobe substantially monotonically decreasing.

The gain conversion characteristics that are corresponded with thetwo-dimensional grayscale conversion characteristics shown in FIG. 12satisfy the relationship of (the output value when US=USO)≧(the outputvalue when US=US1)≧ . . . ≧(the output value when US=USn) for thegrayscale value of the pixels of all input signals IS. The contrast oflocal regions is enhanced by the gain conversion characteristics thatcorrespond with these grayscale conversion characteristics.

In other words, the two-dimensional grayscale conversion characteristicsthat were described in the first embodiment are achieved by multiplyingthe input signal IS with the gain GN, which is determined by the gainconversion characteristics shown in FIG. 12. Consequently, the gainconversion characteristics that are shown in FIG. 12 can be used toachieve the processing of enhancing the contrast of the local area, likein the case described in the first embodiment.

According to the conversion characteristics shown in FIG. 12, when theinput signal IS value (grayscale value) is “a,” the value of the gain GNcan take a value from “R” to “S” depending on the surrounding imagesignal US. That is, even with an input signal IS value (grayscale value)of “a,” the gain GN (gain value) that is output may vary significantlybetween “R” and “S” depending on the surrounding image signal US.

Next, with the multiplication portion 120, the gain GN that has beenobtained is multiplied with the input signal IS to obtain a convertedsignal TS.

Next, the noise reduction degree determining portion 210 inputs theinput signal IS and the gain GN, and from the input signal IS and thegain GN calculates the noise reduction degree NR to be effected on theconverted signal TS. The noise reduction degree determining portion 210then outputs the noise reduction degree NR that it has calculated to thenoise reducing portion 300. In general, the noise component is moresignificantly amplified for pixels the smaller the signal level(grayscale value) of the input signal IS and the larger the gain that isused for grayscale conversion. For this reason, it is preferable toexecute stronger noise reduction processing the smaller the signal level(grayscale value) of the input signal IS and the larger the gain GN ofthe grayscale conversion.

FIG. 13 shows a block diagram of the noise reduction degree determiningportion 210 for performing processing to calculate the noise reductiondegree NR.

As shown in FIG. 13, the noise reduction degree determining portion 210is primarily made of a first noise reduction degree calculation portion201 that calculates a first noise reduction degree NRA from the inputsignal IS, a second noise reduction degree calculation portion 213 thatcalculates a second noise reduction degree NRC from the gain GN, and aminimum value output portion 204 serving as the noise reduction degreedetermining portion that outputs the smaller of the first noisereduction degree NRA and the second noise reduction degree NRC.

Like in the case described in the first embodiment, as shown in FIG. 4,it is preferable that the first noise reduction degree calculationportion 201 outputs a larger noise reduction degree NRA the smaller theinput signal IS. On the other hand, it is preferable that the secondnoise reduction degree calculation portion 213 outputs a larger secondnoise reduction degree NRC the larger the gain GN. An example of theseinput/output properties is shown in FIG. 14.

Next, the minimum value output portion 204 outputs the smaller of thefirst noise reduction degree NRA that has been calculated by the firstnoise reduction degree calculation portion 201 and the second noisereduction degree NRC that has been calculated by the second noisereduction degree calculation portion 213, and thus a larger value iscalculated as the noise reduction degree NR for pixels the smaller thesignal level (grayscale value) of the input signal IS and the larger thegain GN with which processing has been performed. The noise reductiondegree determining portion 210 outputs the noise reduction degree NRthat has been calculated to the noise reducing portion 300. It should benoted that the processing by the minimum value output portion 204 is notlimited to the processing described above, and for example it may alsobe processing in which the mean value (including the arithmetic mean andthe geometrical mean) of the first noise reduction degree NRA and thesecond noise reduction degree NRC is found and that mean value is takenas the noise reduction degree NR, and processing in which the weightedmean of the first noise reduction degree NRA and the second noisereduction degree NRC is found and that weighted mean is taken as thenoise reduction degree NR. Here, the weighted mean is obtained by firstfinding the smaller of the two values for the first noise reductiondegree NRA and the second noise reduction degree NRC (this smaller valueshall be called “value A” and the larger value shall be called “valueB”) and assigning a large weight to the value A and a small weight tothe value B, and then averaging these to obtain the weighted mean. Forexample, it is possible to find the weighted mean through the equation(weighted mean)=((value A)×3+(value B))/4.

Next, the noise reducing portion 300 performs noise reduction processingon the converted signal TS based on the noise reduction degree NR. Thisprocessing is the same as that of the noise reducing portion 300 of thefirst embodiment, and thus will not be described in detail.

As described above, according to the image processing device 2000 ofthis embodiment, the degree with which the noise component of each pixelis amplified can be ascertained from the signal level (grayscale value)of the input signal IS and the value of the gain in a case where theinput signal IS is grayscale converted by the gain into a convertedsignal TS (gain-type grayscale conversion), and thus by executing noisereduction at a strength that is appropriate for the degree to which thenoise component is amplified, it is possible to obtain an output signalOS whose noise component has been suppressed and which has preferablegrayscale characteristics. With the image processing device 2000 of thisembodiment, it is possible to execute preferable gain-type grayscaleconversion on an input signal IS without amplifying the noise componentof the input signal IS, and output this as an output signal OS, and thusby displaying the output signal OS that is output from the imageprocessing device 2000 according to this embodiment on a display device(not shown) as an image (video picture), it is possible to reproduce animage (video picture) that has preferable grayscale characteristics.

It should be noted that the noise reduction degree NR also can becalculated from the gain GN only. By doing this, it is possible toexecute strong noise reduction processing on all pixels that have beenprocessed with a high gain, regardless of the value of the input signalIS.

It is also possible to adjust the strength of the noise reductionprocessing by the noise reducing portion 300 by changing the filtercoefficient of the low-pass filter that is applied to the convertedsignal TS. By doing this, it becomes possible to adjust the degree towhich high-frequency components are reduced by a low-pass filter, andthus the degree to which noise is reduced can be adjusted.

Other Embodiments (Other Modified Examples)

It should be noted that the present invention has been described basedon the above embodiments, but the invention is of course not limited tothe embodiments discussed above. The invention is also inclusive of thefollowing cases.

(1) The above devices specifically are computer systems made from amicroprocessor, ROM, and RAM, for instance. The RAM stores a computerprogram. The microprocessor operates in accordance with the computerprogram, allowing each device to achieve its function. Here, in order toachieve a predetermined function, the computer program is arrived at bycombining a plural number of command codes for indicating an order tothe computer.

(2) Some or all of the structural elements making up the devicesdiscussed above can be constituted by a single system LSI (Large ScaleIntegration). The system LSI is a multifunctional LSI that is producedby integrating a plurality of structural portions on a single chip, andspecifically is a computer system that is constituted by amicroprocessor, ROM, and RAM, for instance. The RAM stores a computerprogram. The microprocessor operates in accordance with the computerprogram, allowing the system LSI to achieve its function.

(3) Some or all of the structural elements making up the devicesdiscussed above can be constituted by an IC card or a single module thatcan be attached to and detached from the devices. The IC card or themodule is a computer system that is constituted by a microprocessor,ROM, and RAM, for instance. The IC card or the module may also includethe multifunctional LSI discussed above. The microprocessor operates inaccordance with a computer program, allowing the IC card or the moduleto achieve its function. It is also possible for the IC card or themodule to be tamper-resistant.

(4) The invention also may be the methods indicated in the abovediscussion. It is possible for these methods to be a computer programthat is achieved by a computer, or a digital signal that is made fromthe computer program. The invention also may be a computer-readablerecording medium, such as a flexible disk, a hard disk, a CD-ROM, a MO,a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray Disc), or a semiconductormemory, on which the computer program or the digital signal is stored.The invention also may be the digital signal stored on these recordingmedia.

The invention can be the computer program or the digital signaltransferred via an electronic transmission line, a wireless or a wiredcommunications line, a network such as the internet, or a databroadcast.

It is also possible for the invention to be a computer system providedwith a microprocessor and a memory, in which the memory stores thecomputer program, and the microprocessor is operated according to thecomputer program.

By storing the program or the digital signal on a recording medium thatis then transported, or by sending the program or the digital signal viaa network, for example, it is possible to run the program or the digitalsignal on independent, separate computer systems.

(5) It is possible to combine the embodiments and the modified examples.

It is possible to conduct some or all of the processing of theembodiments with the pixel serving as the processing unit, and it isalso possible to conduct some or all of the processing with blocks thatare made from a plurality of pixels serving as the processing unit.

Each of the processing functions explained in the aforementionedembodiments may be carried out by hardware, or by software.Alternatively, it may be carried out by mixed processing using thehardware and software.

It should be noted that the specific configuration of the invention isnot limited to the foregoing embodiments, and various changes andmodifications are possible in a range that does not depart from the gistof the invention.

The image processing device, the image processing method, the imageprocessing program, and the integrated circuit of the invention canreproduce an image that has preferable grayscale characteristics withoutamplifying noise components in the image, and thus they are useful inindustrial fields related to image processing, and the image processingdevice, the image processing method, the image processing program, andthe integrated circuit of the invention can be put to use in thosefields.

1. An image processing device comprising: a grayscale conversion portionthat performs grayscale conversion on an input signal that is made frompixel data that forms an image to obtain a converted signal; a noisereduction degree determining portion that determines a noise reductiondegree for the converted signal based on the input signal and theconverted signal; and a noise reducing portion that executes noisereduction processing on the converted signal based on the noisereduction degree.
 2. The image processing device according to claim 1,wherein the grayscale conversion portion performs grayscale conversionbased on histogram information for the grayscale value of the pixel dataof the input signal.
 3. The image processing device according to claim1, wherein the grayscale conversion portion performs grayscaleconversion based on target pixel data of the input signal and a signalthat is obtained by performing predetermined processing on surroundingpixel data around the target pixel data.
 4. The image processing deviceaccording to claim 1, wherein the noise reduction degree determiningportion determines a noise reduction degree based on a ratio between theinput signal and the converted signal.
 5. The image processing deviceaccording to claim 1, wherein the noise reduction degree determiningportion determines a noise reduction degree based on a ratio between theinput signal and the converted signal, and on the input signal. 6.(canceled)
 7. The image processing device according to claim 5, whereinthe noise reduction degree determining portion: sets a gain-based noisereduction degree to a larger value the larger a grayscale conversiongain, which is the ratio of the converted signal to the input signal(=(the converted signal)/(the input signal)); sets a signal level-basednoise reduction degree to a smaller value the larger the signal level ofthe input signal; and determines the noise reduction degree based on thegain-based noise reduction degree and the signal level-based noisereduction degree.
 8. The image processing device according to claim 7,wherein the noise reduction degree determining portion sets the smallerof the gain-based noise reduction degree and the signal level-basednoise reduction degree as the noise reduction degree.
 9. The imageprocessing device according to claim 5, wherein the noise reductiondegree determining portion comprises: a first noise reduction degreecalculation portion that, if the signal level of the input signal islarger than a predetermined signal level threshold, outputs a signallevel-based noise reduction degree whose value is smaller than when thesignal level of the input signal is equal to or less than thepredetermined signal level threshold; a second noise reduction degreecalculation portion that, if a grayscale conversion gain, which is theratio of the converted signal to the input signal (=(the convertedsignal)/(the input signal)), is larger than a predetermined gainthreshold, outputs a gain-based noise reduction degree whose value isgreater than when the grayscale conversion gain is equal to or less thanthe predetermined gain threshold; and a noise reduction degree outputportion that sets a value that has been calculated based on the signallevel-based noise reduction degree and the gain-based noise reductiondegree as the noise reduction degree.
 10. The image processing deviceaccording to claim 9, wherein the noise reduction degree output portionsets the smaller of the signal level-based noise reduction degree andthe gain-based noise reduction degree as the noise reduction degree. 11.An image processing device, comprising: a gain calculation portion thatcalculates a gain for conversion of an input signal that is made frompixel data that forms an image; a multiplication portion that multipliesthe gain with the input signal to obtain a converted signal; a noisereduction degree determining portion that determines a noise reductiondegree for the converted signal based on the gain; and a noise reducingportion that executes noise reduction processing on the converted signalbased on the noise reduction degree.
 12. The image processing deviceaccording to claim 11, wherein the noise reduction degree determiningportion determines a noise reduction degree for the converted signalbased on the input signal.
 13. The image processing device according toclaim 11, wherein the gain calculation portion calculates the gain basedon histogram information for the grayscale values of the pixel data ofthe input signal.
 14. The image processing device according to claim 11,wherein the gain calculation portion calculates the gain based on targetpixel data of the input signal and a signal that is obtained byperforming predetermined processing on surrounding pixel data around thetarget pixel data.
 15. (canceled)
 16. The image processing deviceaccording to claim 12, wherein the noise reduction degree determiningportion: sets a gain-based noise reduction degree to a larger value thelarger gain; sets a signal level-based noise reduction degree to asmaller value the larger the signal level of the input signal; anddetermines the noise reduction degree based on the gain-based noisereduction degree and the signal level-based noise reduction degree. 17.The image processing device according to claim 16, wherein the noisereduction degree determining portion sets the smaller of the gain-basednoise reduction degree and the signal level-based noise reduction degreeas the noise reduction degree.
 18. The image processing device accordingto claim 12, wherein the noise reduction degree determining portioncomprises: a first noise reduction degree calculation portion that, ifthe signal level of the input signal is larger than a predeterminedsignal level threshold, outputs a signal level-based noise reductiondegree whose value is smaller than when the signal level of the inputsignal is equal to or less than the predetermined signal levelthreshold; a second noise reduction degree calculation portion that, ifthe gain is larger than a predetermined gain threshold, outputs again-based noise reduction degree whose value is greater than when thegain is equal to or less than the predetermined gain threshold; and anoise reduction degree output portion that sets a value that has beencalculated based on the signal level-based noise reduction degree andthe gain-based noise reduction degree as the noise reduction degree. 19.The image processing device according to claim 18, wherein the noisereduction degree output portion sets the smaller of the signallevel-based noise reduction degree and the gain-based noise reductiondegree as the noise reduction degree.
 20. An image processing method,which is performed using a processor, the image processing methodcomprising: performing grayscale conversion on an input signal that ismade from pixel data that forms an image to obtain a converted signal;determining a noise reduction degree for the converted signal based onthe input signal and the converted signal; and executing noise reductionprocessing on the converted signal based on the noise reduction degree.21. (canceled)
 22. (canceled)
 23. A non-transitory computer-readablemedium having stored thereon an image processing program, wherein, whenexecuted, the image processing program causes a computer to perform amethod comprising: performing grayscale conversion on an input signalthat is made from pixel data that forms an image to obtain a convertedsignal; determining a noise reduction degree for the converted signalbased on the input signal and the converted signal; and executing noisereduction processing on the converted signal based on the noisereduction degree.
 24. (canceled)
 25. (canceled)
 26. An integratedcircuit comprising: a grayscale conversion portion that performsgrayscale conversion on an input signal that is made from pixel datathat forms an image to obtain a converted signal; a noise reductiondegree determining portion that determines a noise reduction degree forthe converted signal based on the input signal and the converted signal;and a noise reducing portion that executes noise reduction processing onthe converted signal based on the noise reduction degree.
 27. (canceled)